Event-Driven Video Generation
This addresses interaction hallucinations in video generation for AI applications, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the problem of interaction hallucinations in text-to-video models, such as motion starting before contact and objects drifting, by introducing Event-Driven Video Generation (EVD), which improved human preference and VBench dynamics on EVD-Bench, substantially reducing failure modes in state persistence, spatial accuracy, support relations, and contact stability.
State-of-the-art text-to-video models often look realistic frame-by-frame yet fail on simple interactions: motion starts before contact, actions are not realized, objects drift after placement, and support relations break. We argue this stems from frame-first denoising, which updates latent state everywhere at every step without an explicit notion of when and where an interaction is active. We introduce Event-Driven Video Generation (EVD), a minimal DiT-compatible framework that makes sampling event-grounded: a lightweight event head predicts token-aligned event activity, event-grounded losses couple activity to state change during training, and event-gated sampling (with hysteresis and early-step scheduling) suppresses spurious updates while concentrating updates during interactions. On EVD-Bench, EVD consistently improves human preference and VBench dynamics, substantially reducing failure modes in state persistence, spatial accuracy, support relations, and contact stability without sacrificing appearance. These results indicate that explicit event grounding is a practical abstraction for reducing interaction hallucinations in video generation.